MagicQuill: An Intelligent Interactive Image Editing System
This addresses the need for efficient and precise image editing tools for creative professionals, though it appears incremental as it builds on existing MLLM and diffusion methods.
The authors tackled the problem of complex image editing by developing MagicQuill, an interactive system that uses a multimodal large language model to anticipate editing intentions and a diffusion prior with a two-branch plug-in module for precise control, achieving high-quality image edits.
Image editing involves a variety of complex tasks and requires efficient and precise manipulation techniques. In this paper, we present MagicQuill, an integrated image editing system that enables swift actualization of creative ideas. Our system features a streamlined yet functionally robust interface, allowing for the articulation of editing operations (e.g., inserting elements, erasing objects, altering color) with minimal input. These interactions are monitored by a multimodal large language model (MLLM) to anticipate editing intentions in real time, bypassing the need for explicit prompt entry. Finally, we apply a powerful diffusion prior, enhanced by a carefully learned two-branch plug-in module, to process editing requests with precise control. Experimental results demonstrate the effectiveness of MagicQuill in achieving high-quality image edits. Please visit https://magic-quill.github.io to try out our system.